بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
algorithm pca » algorithm a (توسيع البحث), algorithm cl (توسيع البحث), algorithm co (توسيع البحث)
both function » body function (توسيع البحث), growth function (توسيع البحث), beach function (توسيع البحث)
pca function » gpcr function (توسيع البحث), a function (توسيع البحث), fc function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
algorithm pca » algorithm a (توسيع البحث), algorithm cl (توسيع البحث), algorithm co (توسيع البحث)
both function » body function (توسيع البحث), growth function (توسيع البحث), beach function (توسيع البحث)
pca function » gpcr function (توسيع البحث), a function (توسيع البحث), fc function (توسيع البحث)
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101
Parselmouth for bioacoustics: automated acoustic analysis in Python
منشور في 2023"…Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. …"
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102
Prediction performance of different optimization algorithms.
منشور في 2021"…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …"
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103
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104
Comparison of different algorithms.
منشور في 2025"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"
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105
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106
Study proposed algorithm.
منشور في 2025"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …"
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107
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108
ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space
منشور في 2020"…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …"
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109
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110
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111
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112
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113
Algorithm of the brightness scale calibration experiment.
منشور في 2024"…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …"
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114
Gillespie algorithm simulation parameters.
منشور في 2024"…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …"
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115
Scheduling time of five algorithms.
منشور في 2025"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"
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116
Convergence speed of five algorithms.
منشور في 2025"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"
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117
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118
Multi-algorithm comparison figure.
منشور في 2025"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"
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119
Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy
منشور في 2021"…We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. …"
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120
Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy
منشور في 2021"…We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. …"